A Developmental Robotic Paradigm for Mobile Robot Navigation in an Indoor Environment

  • Xiaochun WangEmail author
  • Xiali Wang
  • Don Mitchell Wilkes


In general, traditional machine learning algorithms typically employ task-specific methods and only the parameters pre-determined by the human programmer are updated. These methods often fail to respond to the dynamically changing states of the uncontrolled environments. Additionally, such methods may not represent a developmental entity, such as a human mind. In contrast, an open-ended developmental robot system can learn simple behaviors and buildup more complex behaviors by utilizing the previously learned behaviors. In this chapter, we propose a basic framework for visual learning tasks that integrates a perceptual system into a biologically inspired working memory system. A main objective of this research is to provide a general framework for developmental learning and to investigate how well a neuro-computational PFC working memory model performs on a robotic platform in a real-world environment with complex tasks. Experiments conducted show impressive results.


Autonomous mental development Developmental robotics Machine learning Computer vision Working memory Reinforcement learning TD-learning 


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Copyright information

© Xi'an Jiaotong University Press 2020

Authors and Affiliations

  • Xiaochun Wang
    • 1
    Email author
  • Xiali Wang
    • 2
  • Don Mitchell Wilkes
    • 3
  1. 1.School of Software EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.School of Information EngineeringChang’an UniversityXi’anChina
  3. 3.Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleUSA

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